1. Field
The following description relates to a method and battery system predicting State of Charge (SoC) of a battery.
2. Description of Related Art
Lithium ion (Li-ion) batteries are used in wide variety of applications due to their low weight, high energy density and slow discharge rate. A Li-ion battery (hereafter referred to as “battery”) is generally used in devices such as mobile phones, digital cameras, robotic vacuum cleaners, lawn mowers and electric vehicles. The operation of these devices is substantially dependent on battery power derived from the battery. Thus, updating a user of a device with a present status of the battery is beneficial for enabling the user to seamlessly operate the device.
One of the parameters that may be indicated to the user to indicate the current battery status is a state of charge (SoC) of the battery. For a lithium ion cell or a Li-ion battery, a true SoC would be a measure of the lithium available in electrodes of the battery for chemical reaction to produce electricity. However, the lithium inside the cell is not a measurable quantity. Accordingly, techniques typically equate cell information, such as current, as an estimate of SoC. The estimation of SoC may depend on knowledge of battery capacity. However, the total capacity of the battery decreases while in operation, due to repeated cycling. Capacity fade can be due to side reactions like solid-electrolyte interphase (SEI) film formation. The effects of the side reactions on capacity loss may be considered in such SoC estimations. However, due to ageing, an Open Circuit Voltage (OCV) of the battery can change and may also need or be desirable to be considered during SoC estimation. Another existing technique estimates the SoC based on a charge current, a discharge current, and an output voltage using a nonlinear optimization that expresses a relationship between the SoC and an open circuit voltage (OCV). However, this existing technique utilizes a combination of a nonlinear adaptive filter along with a single particle model (SPM). This filter introduces artificial parameter dependencies and physics. Thus, the filter alters values of parameters of the OCV and the SoC, for matching the measured voltage that may not be reliable or accurate. Further, this existing technique describes the SoC estimation for fresh cells or batteries and does not consider effects on the battery OCV due to aging. Thus, the existing techniques may not provide true SoC estimation.